Introduction
The increasing popularity of remote sensing applications in various scientific fields and themes
can be attributed to significant advancements in sensor technology, automated processing
capabilities, and the widespread availability of free or affordable continuous Earth surface
measurements. This surge in interest is driven by the pressing need for precise and spatially-
explicit information about the Earth's physical characteristics, which is vital for sustainable
development initiatives related to efficient resource utilization, disaster risk reduction, and
ecosystem monitoring and preservation.
However, such use of remotely sensed data requires reliable and quantitative accuracy reports to
support confidence in the information generated. Accuracy assessment and validation are essential
in remote sensing-based projects since decision-making or scientific analysis with data of
unknown or little accuracy will result in information with low reliability, error propagation effects,
and, subsequently, be of limited value.
This manual will walk you through the process of performing area estimation for land use/land
cover, whether for single-date or change detection classifications. We will focus on sample-based
approaches for area estimation, which are preferred over pixel-counting methods due to potential
errors in maps derived from land cover/land use classifications. These errors could be caused by
pixel mixing or noise in the input data.
Using pixel-counting methods can lead to biased estimates of area, without providing information
about whether the estimates are overestimates or underestimates. On the other hand, sample-based
approaches allow us to generate unbiased estimates of area while accounting for the errors
associated with your map.
The primary tool for these exercises is the System for Earth Observation Data Access, Processing,
& Analysis for Land Monitoring (SEPAL). It is a web-based cloud computing platform that
enables users to create image composites, process images, classify images, etc., within the
browser. SEPAL integrates with Collect Earth Online (CEO) and the Google Earth Engine (GEE).
These exercises were adapted from the manual on SEPAL-CEO Area Estimation, Release 3-1-
2021 prepared Dyson and Tenneson (2021).